Research

Annotated deep learning paper implementations with code walkthroughs

Apply computer vision research methods, models, and evaluation tools

Benchmark AI models across 60+ academic evaluation suites and metrics

Apply NLP and text mining techniques to research text data

Clean, recode, and prepare survey response data for analysis

End-to-end data analysis AI agent with Streamlit UI

Clean, transform, and validate messy research data using Stata

Questionnaire and survey design with Likert scales and coding

Data cleaning, transformation, and exploratory analysis with pandas

Diagnose missing data patterns and apply appropriate imputation strategies

Upload messy CSVs with minimal prompting for deep automated analysis

Systematic data cleaning workflows for research datasets

Load, explore, clean, and analyze CSV data with statistical summaries

10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.

Conduct Kaplan-Meier, Cox regression, and time-to-event analyses

Structural equation modeling with latent variables guide

Sample size calculation and statistical power analysis guide

Apply Mann-Whitney, Kruskal-Wallis, and other nonparametric methods

Strategic statistical modeling, experimentation, and causal inference

Plan reproducible ML experiment runs with parameters and metrics tracking

Conduct systematic meta-analyses with effect size pooling and heterogeneity

Statistical hypothesis testing, power analysis, and significance reporting

Detect anomalies and outliers in research data using statistical methods

Bayesian inference methods including prior selection, MCMC, and model comparison